KR102373472B1 - 각 영역에서 최적화된 자율 주행을 수행할 수 있도록 위치 기반 알고리즘 선택을 통해 심리스 파라미터 변경을 수행하는 방법 및 장치 - Google Patents

각 영역에서 최적화된 자율 주행을 수행할 수 있도록 위치 기반 알고리즘 선택을 통해 심리스 파라미터 변경을 수행하는 방법 및 장치 Download PDF

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KR102373472B1
KR102373472B1 KR1020200004517A KR20200004517A KR102373472B1 KR 102373472 B1 KR102373472 B1 KR 102373472B1 KR 1020200004517 A KR1020200004517 A KR 1020200004517A KR 20200004517 A KR20200004517 A KR 20200004517A KR 102373472 B1 KR102373472 B1 KR 102373472B1
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cnn
autonomous driving
target vehicle
learning
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김용중
김인수
김학경
남운현
부석훈
성명철
유우주
정경중
제홍모
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주식회사 스트라드비젼
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    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
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    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
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    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
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KR1020200004517A 2019-01-30 2020-01-13 각 영역에서 최적화된 자율 주행을 수행할 수 있도록 위치 기반 알고리즘 선택을 통해 심리스 파라미터 변경을 수행하는 방법 및 장치 KR102373472B1 (ko)

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US201962798821P 2019-01-30 2019-01-30
US62/798,821 2019-01-30
US16/731,083 US10890916B2 (en) 2019-01-30 2019-12-31 Location-specific algorithm selection for optimized autonomous driving
US16/731,083 2019-12-31

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KR102373472B1 true KR102373472B1 (ko) 2022-03-14

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US (1) US10890916B2 (zh)
EP (1) EP3690725A1 (zh)
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Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102598402B1 (ko) * 2018-07-24 2023-11-06 현대자동차 주식회사 기어 검사장치 및 이를 이용한 기어 검사방법
US11880997B2 (en) 2020-08-28 2024-01-23 Samsung Electronics Co., Ltd. Method and apparatus with pose estimation
US11938941B2 (en) 2020-08-31 2024-03-26 Denso International America, Inc. Mode selection according to system conditions
KR102345267B1 (ko) * 2020-10-12 2021-12-31 서울대학교산학협력단 목표 지향적 강화학습 방법 및 이를 수행하기 위한 장치
KR102588450B1 (ko) * 2021-11-02 2023-10-13 주식회사 오비고 특정 사용자의 자율주행 경험 만족도 제고를 위한 자율주행 알고리즘 개인화 방법 및 장치
KR102421289B1 (ko) * 2022-01-03 2022-07-18 주식회사 아라종합기술 패러렐 디시전 보팅 알고리즘에 따른 영상기반 시정 탐지 학습 방법 및 학습 장치, 그리고 이를 이용한 테스트 방법 및 테스트 장치
CN117593686B (zh) * 2024-01-19 2024-04-09 福思(杭州)智能科技有限公司 基于车况真值数据的模型评测方法和装置

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10176388B1 (en) * 2016-11-14 2019-01-08 Zoox, Inc. Spatial and temporal information for semantic segmentation

Family Cites Families (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010049323A1 (en) * 2000-02-10 2001-12-06 Fox Gary T. Treadmill
US10019652B2 (en) * 2016-02-23 2018-07-10 Xerox Corporation Generating a virtual world to assess real-world video analysis performance
CN107180220B (zh) * 2016-03-11 2023-10-31 松下电器（美国）知识产权公司 危险预测方法
US10390003B1 (en) * 2016-08-29 2019-08-20 Perceptln Shenzhen Limited Visual-inertial positional awareness for autonomous and non-autonomous device
US20180239969A1 (en) 2017-02-23 2018-08-23 Ford Global Technologies, Llc Free Space Detection Using Monocular Camera and Deep Learning
US10108850B1 (en) * 2017-04-24 2018-10-23 Intel Corporation Recognition, reidentification and security enhancements using autonomous machines
US10007269B1 (en) 2017-06-23 2018-06-26 Uber Technologies, Inc. Collision-avoidance system for autonomous-capable vehicle
US10474160B2 (en) 2017-07-03 2019-11-12 Baidu Usa Llc High resolution 3D point clouds generation from downsampled low resolution LIDAR 3D point clouds and camera images
US10496104B1 (en) * 2017-07-05 2019-12-03 Perceptin Shenzhen Limited Positional awareness with quadocular sensor in autonomous platforms
US10192113B1 (en) * 2017-07-05 2019-01-29 PerceptIn, Inc. Quadocular sensor design in autonomous platforms
US10095977B1 (en) * 2017-10-04 2018-10-09 StradVision, Inc. Learning method and learning device for improving image segmentation and testing method and testing device using the same
US10089743B1 (en) * 2017-10-05 2018-10-02 StradVision, Inc. Method for segmenting an image and device using the same
US10169679B1 (en) * 2017-10-13 2019-01-01 StradVision, Inc. Learning method and learning device for adjusting parameters of CNN by using loss augmentation and testing method and testing device using the same
US10049323B1 (en) * 2017-10-13 2018-08-14 StradVision, Inc. Method and device for performing activation and convolution operation at the same time and learning method and learning device for the same
US10007865B1 (en) * 2017-10-16 2018-06-26 StradVision, Inc. Learning method and learning device for adjusting parameters of CNN by using multi-scale feature maps and testing method and testing device using the same
US10902615B2 (en) * 2017-11-13 2021-01-26 Qualcomm Incorporated Hybrid and self-aware long-term object tracking
US11080886B2 (en) * 2017-11-15 2021-08-03 Qualcomm Incorporated Learning disentangled invariant representations for one shot instance recognition
US10535138B2 (en) * 2017-11-21 2020-01-14 Zoox, Inc. Sensor data segmentation
US10324467B1 (en) * 2017-12-29 2019-06-18 Apex Artificial Intelligence Industries, Inc. Controller systems and methods of limiting the operation of neural networks to be within one or more conditions
JP7011472B2 (ja) 2018-01-15 2022-01-26 キヤノン株式会社 情報処理装置、情報処理方法
US20190361454A1 (en) * 2018-05-24 2019-11-28 GM Global Technology Operations LLC Control systems, control methods and controllers for an autonomous vehicle
US10311578B1 (en) * 2019-01-23 2019-06-04 StradVision, Inc. Learning method and learning device for segmenting an image having one or more lanes by using embedding loss to support collaboration with HD maps required to satisfy level 4 of autonomous vehicles and softmax loss, and testing method and testing device using the same

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10176388B1 (en) * 2016-11-14 2019-01-08 Zoox, Inc. Spatial and temporal information for semantic segmentation

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CN111507373A (zh) 2020-08-07
US20200241544A1 (en) 2020-07-30
US10890916B2 (en) 2021-01-12
CN111507373B (zh) 2023-09-08
EP3690725A1 (en) 2020-08-05
KR20200094655A (ko) 2020-08-07
JP7092383B2 (ja) 2022-06-28
JP2020123346A (ja) 2020-08-13

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